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PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors

Leah Weber, Nuraini Aguse, Nicholas Chia, View ORCID ProfileMohammed El-Kebir
doi: https://doi.org/10.1101/2020.03.30.016410
Leah Weber
1Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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Nuraini Aguse
1Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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Nicholas Chia
2Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
3Division of Surgical Research, Department of Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
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Mohammed El-Kebir
1Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
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  • ORCID record for Mohammed El-Kebir
  • For correspondence: melkebir@illinois.edu
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Abstract

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE’s computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

Author summary Cancer development in a patient can be explained using a phylogeny — a tree that describes the evolutionary history of a tumor and has therapeutic implications. A tumor phylogeny is constructed from sequencing data, commonly obtained using either bulk or single-cell DNA sequencing technology. The accuracy of tumor phylogeny inference increases when both types of data are used, but single-cell sequencing may become prohibitively costly with increasing number of cells. Here, we propose a method that uses bulk sequencing data to guide the design of a follow-up single-cell sequencing experiment. Our results suggest that PhyDOSE provides a significant decrease in the number of cells to sequence compared to the number of cells sequenced in existing studies. The ability to make informed decisions based on prior data can help reduce the cost of follow-up single cell sequencing experiments of tumors, improving accuracy of tumor phylogeny inference and ultimately getting us closer to understanding and treating cancer.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Additional real patient data as well as new simulations are used in the analysis of PhyDOSE.

  • ↵1 https://www.ibm.com/analytics/cplex-optimizer

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 19, 2020.
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PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors
Leah Weber, Nuraini Aguse, Nicholas Chia, Mohammed El-Kebir
bioRxiv 2020.03.30.016410; doi: https://doi.org/10.1101/2020.03.30.016410
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PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors
Leah Weber, Nuraini Aguse, Nicholas Chia, Mohammed El-Kebir
bioRxiv 2020.03.30.016410; doi: https://doi.org/10.1101/2020.03.30.016410

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